SimpleOne explains why vibe coding speeds up releases but hurts code quality and review

SimpleOne has published a breakdown of vibe coding that skips the usual war between camps. The company admits that generating code with AI really does speed up product releases, but the price is paid in code quality, review complexity and growing dependence on external models.

Why developers disagree

Engineers' main complaint is not that AI writes broken code, but that it often writes code that is bloated and noisy. The article gives a simple example: a task to add a discount for a user turns into a function with several levels of nesting, repeated checks and comments that merely retell the code. Such a PR can be merged because the tests are green, but it is noticeably harder to read, extend and review. As a result, tech debt piles up not from one big failure but from hundreds of small "well, it seems to work" decisions.

The second problem is a shift in the developer's role from author of the solution to chat operator. When a person less and less often thinks through the architecture and algorithm themselves, they lose the ability to explain why the system is built the way it is. This is especially painful during onboarding and interviews, where it matters not just to show a result but to defend your line of reasoning.

The third complaint concerns business risk: the strongest models belong to external providers, so the speedup comes with dependence on someone else's infrastructure, data retention policies and legal restrictions.

Why business won't wait

But business measures success differently. If a product can be built in a week instead of a quarter, the team reaches the market sooner, gets feedback faster and learns earlier whether there is any demand at all. The piece ties this to the overall pace of the technology race: there are more startups, competition is tighter and the window for entering a market is shorter. So the question for management is not "is this code perfect?" but "will we take the niche before a competitor does, and will we avoid burning extra months of development on it?"

• Build the first prototype faster and show it to users
• Test a hypothesis more cheaply before hiring a full development team
• Rewrite a failed implementation faster after the first feedback
• Reach customers sooner, start selling and collect real data

The authors go further and state a thought that is uncomfortable for the engineering community: the market increasingly forgives products their rough edges if they solve an important problem. They cite services and tools that crash or lag from time to time but retain their audience through usefulness. For internal systems, B2C apps and quick prototypes, this shifts the balance: sometimes it pays more for a company to accept imperfect code now than to polish the architecture for a long time and miss the release. However, for banks, medicine and government infrastructure, this approach is still too risky.

Where the line falls

SimpleOne's key conclusion is that arguing about vibe coding in terms of "good" and "evil" is pointless. It is not a replacement for all development but a tool for a specific class of tasks. Where the cost of error is moderate, the product lives in short iterations, and the code may have to be rewritten anyway, generating it with AI can be a rational choice. Where a failure hits money, security or regulatory compliance, human engineering remains mandatory.

"The goal of vibe coding is to quickly deliver a cheap working product."

The article's practical recipe is also fairly down-to-earth: don't drag this approach straight into the core of the business, but test it on internal tools, employee interfaces, prototypes and auxiliary services. If time to first result drops sharply while the number of bugs stays manageable, the scope can be expanded. If review is overwhelmed, production incidents grow and the team stops understanding its own code, the limit has already been reached. In this sense, vibe coding requires not faith but metrics and discipline.

What this means

Vibe coding is unlikely to kill the developer profession, but it definitely changes the criteria for choosing between speed and quality. For part of the market, "good enough" AI code will become the norm, especially in prototypes and internal products. For critical systems, requirements will not change: reliability, explainability and control still matter more there. The main question now is not whether a team likes the approach itself, but where its price is justified and where it no longer is. The teams that benefit most will be those that learn to honestly separate quick experiments from zones with a high cost of error.